Selective dry-cow therapy can be implemented successfully in cows of all milk production levels
Bibliographic record
Abstract
Antibiotic stewardship on dairy farms can be heightened through the implementation of selective dry-cow therapy (SDCT). However, some producers are concerned that this practice may be related to poor udder health outcomes in cows with high milk production at the time of dry-off. The objective of this study was to evaluate if the effect of culture-guided SDCT (Cult-SDCT) and algorithm-guided SDCT (Alg-SDCT) on dry-period intramammary infection (IMI) dynamics and postcalving udder health and performance [when compared with blanket dry-cow therapy (BDCT)] varied according to milk production level before dry-off. Data were compiled from clinical trials conducted in the United States and Canada that compared Cult-SDCT and Alg-SDCT to a positive control, i.e., BDCT. In those trials, cows were enrolled 1-2 d before dry-off, randomized to their dry-cow therapy strategy and followed until 120 d in milk of the subsequent lactation. The number of cows and quarters in the final data set were 1,485 and 5,097, respectively. Measured outcomes included quarter-level antibiotic use at dry-off, quarter-level IMI prevalence after calving, quarter-level dry-period IMI cure risk, quarter-level dry-period new IMI risk, cow-level clinical mastitis and removal from the herd during 1-120 d in milk, and somatic cell count and milk yield during 1-120 DIM. The primary objective of analysis was to investigate if the effect of Cult-SDCT and Alg-SDCT on these outcomes, when compared with BDCT, varied according to milk production level before dry-off. To do this, each cow was classified as having low, mid or high production, based on her milk yield tertile group at the most recent herd test before enrollment (low: <23.7 kg/d, mid: 23.7 to 30.4 kg/d, and high >30.4 kg/d). Multivariable generalized estimating equations were used to estimate risk differences and differences in means, and Cox regression was used to estimate hazard ratios. For Cult-SDCT, the proportion of quarters treated with dry-cow antibiotics within each milk production level were 40.7% (low), 41.7% (mid) and 47.2% (high). For Alg-SDCT, the proportions were 60.6% (low), 38.7% (mid), and 35.1% (high). Measures of udder health were not markedly different when comparing Cult-SDCT to BDCT and Alg-SDCT to BDCT. This was consistently observed in low, mid and high producing cows. In conclusion, the findings from this study indicate that Cult-SDCT and Alg-SDCT can be successfully implemented in cows of all milk production levels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".